Tzu-Mao Li

tzumao@berkeley.edu
CV

I am a postdoc at the EECS department of UC Berkeley, working with Jonathan Ragan-Kelley. My work is at the intersection of computer graphics, computational photography, and programming systems. Specifically, I develop efficient sampling methods for light transport simulation, and programming systems that extract domain knowledge from graphics and image processing algorithms (through, for example, automatic differentiation). I did my Ph.D. in the computer graphics group at MIT CSAIL, advised by Frédo Durand. I received my B.S. and M.S. degree in computer science and information engineering from National Taiwan University in 2011 and 2013, respectively. During my time at National Taiwan University, I was a member of the graphics group at Communication and Multimedia Lab, where I worked with Yung-Yu Chuang.

Publications

Code, slides, video, papers are in the project pages.

Learning to Optimize Halide with Tree Search and Random Programs
Andrew Adams, Karima Ma, Luke Anderson, Riyadh Baghdadi, Tzu-Mao Li, Michaël Gharbi, Benoit Steiner, Steven Johnson, Kayvon Fatahalian, Frédo Durand, Jonathan Ragan-Kelley
ACM Transactions on Graphics (Proceedings of SIGGRAPH 2019)
The first Halide autoscheduler that produces faster code comparing to human experts on average.
Sample-based Monte Carlo Denoising using a Kernel-Splatting Network
Michaël Gharbi, Tzu-Mao Li, Miika Aittala, Jaakko Lehtinen, Frédo Durand
ACM Transactions on Graphics (Proceedings of SIGGRAPH 2019).
Permutation invariant mapping from Monte Carlo samples to an image through splatting.
Differentiable Visual Computing [slides (Keynote)] [slides (Powerpoint)]
Tzu-Mao Li
MIT PhD Dissertation
A coherent view of my PhD research. It has some new discussions regarding previous papers, and some background reviews.
Inverse Path Tracing for Joint Material and Lighting Estimation
Dejan Azinović, Tzu-Mao Li, Anton Kaplanyan, Matthias Nießner
Conference on Computer Vision and Pattern Recognition (CVPR), 2019 (oral presentation)
Applying differentiable rendering for material and lighting reconstruction.
Differentiable Monte Carlo Ray Tracing through Edge Sampling
Tzu-Mao Li, Miika Aittala, Frédo Durand, Jaakko Lehtinen
ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2018)
Computing gradients of the light transport equation through an explicit sampling of Dirac delta functions on triangle edges.
Differentiable Programming for Image Processing and Deep Learning in Halide
Tzu-Mao Li, Michaël Gharbi, Andrew Adams, Frédo Durand, Jonathan Ragan-Kelley
ACM Transactions on Graphics (Proceedings of SIGGRAPH 2018)
Halide meets automatic differentiation.
Aether: An Embedded Domain Specific Sampling Language for Monte Carlo Rendering
Luke Anderson, Tzu-Mao Li, Jaakko Lehtinen, Frédo Durand
ACM Transactions on Graphics (Proceedings of SIGGRAPH 2017)
A programming language for Monte Carlo rendering that automatically computes the probability density of a light path sample.
Anisotropic Gaussian Mutations for Metropolis Light Transport through Hessian-Hamiltonian Dynamics
Tzu-Mao Li, Jaakko Lehtinen, Ravi Ramamoorthi, Wenzel Jakob, Frédo Durand
ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2015)
A variant of Metropolis light transport algorithm that makes use of automatically differentiated Hessian matrix of light path contribution.
Dual-Matrix Sampling for Scalable Translucent Material Rendering
Yu-Ting Wu, Tzu-Mao Li, Yu-Hsun Lin, and Yung-Yu Chuang
IEEE Transactions on Visualization and Computer Graphics (TVCG), 2015
Subsurface scattering with many-lights using matrix sampling.
SURE-based Optimization for Adaptive Sampling and Reconstruction
Tzu-Mao Li, Yu-Ting Wu, Yung-Yu Chuang
ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2012)
Stein's unbiased risk estimator for sampling and denoising in Monte Carlo rendering.

Word cloud

Some keywords extracted from the publications above. They might give you some sense of my research.

Misc

Graphics bibtex
A mega bibtex file containing many graphics-related literatures.
Joint Stein’s Unbiased Risk Estimation for Adaptive Sampling and Reconstruction
A short note on a generalized formulation of our SURE-based rendering method.
dpt
My prototypical renderer.
smallgdpt
Gradient-Domain Path Tracing in ~450 lines.